Computer-aided detection of lung nodules

ABSTRACT

The invention relates to a method and a device for forming an image of body structures from an image data set, notably for highlighting potential nodular structures (KI; KA) in a lung. The problem to be solved by the invention is to achieve automatic highlighting of potential nodular structures in methods of this kind. This is realized in that in a plurality of steps a binary data set is formed in which all pixels present in the image data set are subdivided into pixels to be marked and those not to be marked, a first filtering operation being performed in which for each pixel (D) there is determined a distance value which corresponds to the shortest distance between the pixel and the edge (KAG) of the image structure (KA) in which the pixel is situated, those pixels being selected from the binary data set whose distance value is below a predetermined distance limit value, there being performed a second filtering operation in which those previously selected pixels remain selected which are directly neighbored by two pixels having a smaller distance value in both directions of at least one straight line which extends through the pixel, there being performed a third filtering operation in which those previously selected pixels remain selected for which the surrounding pixels, being situated at a distance corresponding to the distance value of the pixel, have a distance value which is a predetermined distance difference value smaller than the distance value of the pixel to be tested itself, the pixels thus selected being used to form an image in which the selected pixels are highlighted.

The invention relates to a method and a device for forming an image ofbody structures from an image data set. The invention is intendednotably to highlight potential nodular structures in a lung.

Methods of the kind set forth are known for numerous applications in thefield of medical imaging. In known methods a two-dimensionalrepresentation of the image elements or pixels contained in an imagedata set is formed from the image data set. Regularly a detail, aperspective and an enlargement factor can be chosen by a user. It isalso known to perform a filtering operation on the pixels during theformation of the image in order to exclude pixels which are irrelevantfor the diagnosis from the image.

In the known methods for forming an image of body structures often theproblem is encountered that the viewer of the image is presented with alarge amount of image information and that it is difficult to discovergiven, notably small image structures in the image. Small, enclosed bodystructures within the image, however, are often of high diagnosticrelevance, because they represent early stages of pathologicaldevelopments. Recognition of such pathological structures at an earlystage is often a prerequisite for the success of therapeutical steps.

In known methods for marking such small structures in images of bodystructures a plurality of filtering steps are carried out successivelyin order to filter out the relevant small structures. A method of thiskind can be found, for example, in the article “Object based deformationtechnique for 3-D CT lung nodule detection”, by Shyh Liang Lou et al.,Proceedings of the SPIE Vol. 3661, PT. 1-2, pp. 1544 to 1552, 1999.According to the known method an isolated representation of the organ tobe observed is regularly obtained by means of pre-filtering steps andsubsequently a search is performed within this organ, using geometricalcriteria, so as to find structures of a given type, notably roundstructures. This approach utilizes complex geometrical selection andexclusion criteria which cannot be followed by a user of the method.Furthermore, the known methods do not ensure a reliable representationof all potential nodular structures. The trust of the user in theautomatic filter steps for the pre-selection of potential pathologicalstructures, therefore, is limited and further reduced by the fact thatthe method is not transparent.

Therefore, it is an object of the invention to provide a method and adevice for the formation of an image of body structures which enablesmore reliable marking of potential pathological image structures incomparison with known methods.

The object is achieved by means of a method of the kind set forth whichcomprises the following steps:

-   a) forming a binary data set in which the pixels present in the    image data set are subdivided into pixels which are to be marked and    those which are not to be marked, in which step    -   a1. a first filtering operation is performed in which a distance        value is determined for each pixel, which distance value        corresponds to the shortest distance between the pixel and the        edge of the image structure in which the pixel is situated and        in which those pixels whose distance value is below a        predetermined distance limit value are selected from the binary        data set,    -   a2. a second filtering operation is performed in which those        previously selected pixels remain selected which are directly        neighbored, in both directions of at least one straight line        extending through the pixel, by two pixels having a smaller        distance value,    -   a3. a third filtering operation is performed in which those        previously selected pixels remain selected for which the        surrounding pixels, situated at a distance corresponding exactly        to the distance value of the pixel, have a distance value which        is a predetermined distance difference value smaller than the        distance value of the pixel to be tested itself,-   b) forming a marked image data set from the image data set by    marking the pixels which have been selected after the last filtering    operation in the binary data set, and-   c) forming the image of the body structure from the marked image    data set.

The method in accordance with the invention enables the formation of animage of body structures in which given pixels are marked, notablyhighlighted, that is, pixels which are of primary importance for theevaluation of pathological structures. The pixels to be marked aredetermined from the total number of pixels of the image data set bymeans of three filtering operations which can be simply followed.

During a first filtering operation all pixels are determined which aresituated less than a predetermined distance from the edge of the imagestructure in which they are present. All pixels of image structures canthus be determined whose dimensions are less than twice thepredetermined distance limit value. Furthermore, this first filteringoperation determines all pixels which are situated in the edge zone oflarge image structures, said edge zone having a thickness correspondingto the distance limit value.

In this context an image structure is to be understood to mean acoherent surface which is formed by a plurality of neighboring pixelswhich on the one hand neighbor one another and, moreover, presentthemselves to a viewer as being coherent on the basis of a secondcriterion. This second criterion may be, for example, correspondence orsimilarity of the image value of the pixels. Furthermore, other knownfilter methods can be used as a second criterion, for example, the watershed transformation, the morphological aperture or similar methods.

During the second filtering operation, all those pixels which representa local distance value maximum in at least one direction are determinedfrom the pixels determined during the first filtering operation. In atleast two opposite directions, that is, in a first direction and in asecond direction which deviates 180° therefrom, these pixels aresurrounded directly by pixels which are situated at a distance from theedge of the image structure which is smaller than the distance value ofthe pixel itself.

Thus, during the second filtering operation all pixels are selectedwhich represent the center or the center line of a small imagestructure. Visually speaking, if the distance values of the pixels areseen as altitude values in a topographic map, the pixels situated on amountain ridge or a mountain top are selected during the secondoperation.

During the third filtering operation, subsequent to the second filteringoperation, from among these pixels there are selected those pixels whichare not enclosed by any pixel within a distance which correspondsexactly to their distance value in an arbitrary direction, that is, byno pixel whose distance value is equal to the distance value of thepixel itself or deviates only slightly therefrom. Thus, all thoseelements are rejected which have been selected after the secondfiltering operation and are situated in the edge zone of a large imagestructure, and hence are enclosed in at least one direction, that is, inthe direction towards said larger image structure, by a pixel at therelevant distance whose distance value is significantly larger than thedistance value of the pixel itself.

Furthermore, the third filtering operation excludes all pixels which arenot situated at the center of an approximately round image structure;the selection of the permissible distance difference value also leavingpixels of a structure selected when the structure deviates to a givendegree from the ideal round structure. Visually speaking, in thepreviously described topographic map those pixels are excluded whichrepresent the mountain ridge lines in elongate image structures, whereasthe pixels which represent the top in round, oval or similarly boundedimage structures remain selected.

After the third filtering operation, a deliberately reduced number ofpixels has been selected from the number of many, possibly relevantpixels, that is, by exclusion of irrelevant pixels, said selected pixelsqualifying as centers of potential nodular structures. These pixels arethen marked in a new image data set to be formed and hence can berepresented in a separate way upon formation of the image of the bodystructure from this marked image data set. These pixels can notably bemarked in color, be highlighted with enriched contrast, or be indicatedby alternating fading in and out or in another manner.

In a first advantageous version of the method of the invention the firstfiltering operation is preceded by a filtering operation by means oflimit values of the image values. This method of filtering, also knownas thresholding, enables the selection of all pixels above a first imagevalue limit value and/or below a second image value limit value in asimple filtering step. In as far as the image data set was acquired byway of computer tomography, this type of filtering can be applied byselecting pixels whose Hounsfield unit (HU) is below a predetermined,first HU and/or whose HU is above a second HU.

In a second advantageous further version of the method in accordancewith the invention the pixel to be tested during the third filteringoperation is rejected as not to be marked as soon as a surrounding pixelis found whose distance value is the predetermined distance differencevalue smaller than the distance value of the pixel to be tested itself.The filtering is accelerated as a result of this further development.Because a pixel is rejected during the third filtering operation if onlyone of the pixels situated at the distance corresponding tot thedistance value has a distance value which is the predetermined distancedifference value smaller, further testing of the pixels surrounding thepixel at a corresponding distance can be dispensed with as soon as thecorresponding enclosing pixel is found. The required calculationcapacity is thus reduced or the calculation time is reduced.

The method in accordance with the invention can be advantageouslyelaborated when the predetermined distance difference value is zero. Itis thus achieved that each pixel which is surrounded, at a distancewhich corresponds exactly to the distance value, by pixels whosedistance value deviates from the distance value of the pixel itself,remains selected and only those pixels are rejected which are surroundedby at least one pixel, at the corresponding distance, whose distancevalue is identical to that of the pixel itself.

A further advantageous version of the method in accordance with theinvention comprises a fourth filtering operation in conformity with thecharacterizing part of claim 5. According to this further elaboration,pixels are excluded from the selection remaining after the thirdfiltering operation if a pixel having a higher distance value is presentwithin their image structure and/or if the image structure extends formore than a predetermined value in an arbitrary direction, meaning thatthe image structure exceeds a given size in an arbitrary direction.

In that case it can notably be arranged that the predetermined volumedistance value corresponds to five times the distance value of the startelement. It has been found that this limit value is suitable to excludepixels of irrelevant image structures from the selection, that is,notably when potential nodular structures in the lung are to be marked.

The fourth filtering method can be further elaborated in that the regiongrowth takes place point-symmetrically relative to the start element andthe start element is rejected as not to be marked as soon as the meanvalue of the distance values of the included image elements is largerthan a predetermined distance mean value which advantageouslycorresponds to the distance value of the start element.

In this context point-symmetrical growth is to be understood to meanthat in a first step all elements which directly adjoin the startelement are included by the region growth, that in a subsequent step allouter elements which directly adjoin these elements are included by theregion growth, and so on. The mean value of the distance value of theincluded pixels is then the mean value of the distance value of allpixels included by the region growth.

It has been found that image structures which are point-symmetricallyenclosed by such a region growth regularly are not potentialpathological structures if at an arbitrary instant of the region growththe mean value of the distance value of all pixels included is largerthan the distance value of the start element, that is, in as far as thestart element is chosen to be one of pixels selected after the fourthfiltering operation. This further version of the method in accordancewith the invention thus refines the selection of potentiallypathological nodular structures.

The described method can advantageously be elaborated in conformity withthe characterizing part of claim 9.

The distance value sum then represents the sum of all distance values ofthose pixels which have been newly included in a phase of the regiongrowth. These pixels are regularly situated on a geometry which ispoint-symmetrical relative to the start element, that is, on a circle inthe case of a two-dimensional image and on a spherical surface in thecase of a three-dimensional image. The distance value sum of the firstphases of the region growth is then regularly larger than the distancevalue of the start element itself, because the sum of the plurality ofpixels around the start element is larger than the distance value of thestart element itself, even when the distance value of the individualpixels is smaller than that of the start element. The rise of the curvereaches a maximum and decreases again, in as far as the region growthtakes place in a small, enclosed image structure, when the region growthincludes increasingly pixels with small distance values.

The formation of a minimum in the curve and the subsequent rise of thecurve can take place when pixels having larger distance values areincluded again by the region growth.

It has been found that the ratio of the distance value sum at a maximumto a ratio of the distance value sum at a subsequent minimum can be usedas a criterion in respect of the probability that the start elementbelongs to a potential nodular structure. This is particularly unlikelywhen the distance value sum at the minimum is not larger than half thedistance value sum at the maximum.

Furthermore, for a further advantageous development of the method it hasbeen found that the quotient of the mean value of the distance values ofall pixels included by the region growth until the local minimum isreached and the distance value of the start element can be used todetermine the probability that the start element belongs to a potentialnodular structure. It is particularly unlikely that the start elementbelongs to a potential nodular structure if the mean value of thedistance values of all pixels included until the local minimum isreached is larger than twice the distance value of the start element.

The above method is advantageously further elaborated by defining thepredetermined extreme value quotient to be 0.35. The filtering thenobtained is finer than when the extreme value quotient is determined soas to be 0.5.

The method in accordance with the invention is further elaborated bycarrying out a fifth filtering operation as disclosed in thecharacterizing part of claim 11. As a result of this elaboration it isachieved that pixels remain selected which were rejected in the fourthfiltering operation on the basis of one of the conditions stated in theclaims 5 to 10 and described above, because in the course of the regiongrowth a second image structure which adjoins the first image structureor is directly fused therewith was included by the region growth. Thismay occur, for example, in the case of nodular structures which aresituated in the wall region of a large image structure and are fusedtherewith or are connected thereto via vessels.

In this case the fourth filtering operation leads to the rejection ofpixels of potential nodular structures, so that an incorrect diagnosiscould be made. The fifth filtering operation imposes a boundary which issituated as exactly as possible at the transition between the firstimage structure and the second image structure. Subsequently, as in thefourth filtering operation, region growth is started as from a startelement formed by the pixels remaining after the third filteringoperation, the start element being rejected as not to be marked if atleast one of the conditions of the fourth filtering operation accordingto the claims 5 to 10 is satisfied. However, pixels which, viewed fromthe start element, are situated to the other side of the boundary remainrejected according to this region growth, that is, pixels situated inthe larger structure with which the potential nodular structure isfused.

The above method can be advantageously further elaborated by determiningthe boundary on the basis of the steps disclosed in claim 12. In thismethod of drawing a boundary, a plane boundary is drawn in that firstthose pixels are determined which are situated in the edge zone of thesecond image structure included by the region growth and are alsosituated at the edge of the region growth at the instant of theinterruption conditions of the fourth filtering operation. Subsequently,a straight line (in the case of two-dimensional image processing) or aplane (in the case of three-dimensional image processing) is determined,which line is situated in space in such a manner that the sum of alldistances between the straight line or the plane and the previouslydetermined pixels is minimum. This plane or line constitutes a very goodapproximation of the transition between the first and the second imagestructure and hence can be used as a boundary line or plane.

A further aspect of the invention concerns a device which comprisesmeans for carrying out the described steps of the method as well as acomputer program with program means for carrying out said steps of themethod.

The invention provides a method for forming images of body structureswhereby a sensible reduction of the image data can be carried out bymeans of filtering steps which can be simply followed and understood andin which those pixels which represent potential nodular structures canbe filtered out. The pixels thus filtered out can be represented eitherin isolated form or be highlighted, marked or otherwise identified in anoverall image. The method in accordance with the invention thus enablesa particularly reliable and fast localization of small, circumscribednodular structures by a physician. The simply understood filtering stepsof the method in accordance with the invention are suitable to enhancethe trust of the user in the automatic detection of the nodularstructures.

An advantageous version of the method will be described with referenceto the accompanying Figures. Therein:

FIG. 1 shows a computer tomographic horizontal slice image of the regionof the lungs of a human,

FIG. 2 shows an image as shown in FIG. 1 after filtering by means ofimage value limits,

FIG. 3 shows an image as shown in FIG. 1 after the filling of the spacesurrounding the body,

FIG. 4 shows an image as shown in FIG. 1 with distance values,

FIG. 5 is a diagrammatic representation of a nodule fused with the walland a segment of a vessel,

FIG. 6 is a diagrammatic representation of the point-symmetrical growtharound a start element,

FIG. 7 shows a detail of the start element of FIG. 1 situated at thecross hairs in an isolated nodule,

FIG. 8 shows the variation of the mean distance value in the growthenvelope for a point-symmetrical growth starting from the start elementcharacterized in FIG. 7,

FIG. 9 shows the variation of the distance value sum in the growthenvelope for the start element shown in FIG. 7,

FIGS. 10 to 12 show the procedure for determining a boundary in the caseof nodular structures fused with larger structures,

FIG. 13 shows a detail of FIG. 1 with a start element situated at thecross hairs in a nodule fused with the wall structure,

FIG. 14 shows the variation of the mean distance value in the growthenvelope for the start element shown in FIG. 13,

FIG. 15 shows the variation of the distance value sum in the growthenvelope for the start element shown in FIG. 13,

FIG. 16 shows the variation of the mean distance value in the growthenvelope for the start element of FIG. 13 after the drawing of aboundary in conformity with the FIGS. 10 to 12,

FIG. 17 shows the variation of the distance value sum in the growthenvelope for the start element of FIG. 13 after the drawing of theboundary in conformity with the FIGS. 10 and 11,

FIG. 18 shows a slice image as shown in FIG. 1 with marked potentialnodule structures, and

FIG. 19 shows a detail of FIG. 18.

FIG. 1 is a horizontal sectional view of the body of a human. The imagedata wherefrom the slice image of FIG. 1 has been formed was acquired bymeans of a computer tomography X-ray method. The individual bodystructures are reproduced in corresponding shades of gray in dependenceon their specific radiation transmissivity for X-rays. These shades ofgray are referred to as Hounsfield Unit (HU) values. The image shown inFIG. 1 offers an expert physician a variety of information wherefrom thephysician can derive specific information, such as the presence ofdiseased nodular structures, only by a thorough and time-consumingstudy.

In order to facilitate the extraction of such information, a filteringmethod as described in detail hereinafter is employed.

To this end, first a black/white image with binary image datainformation is formed from the gray scale image of FIG. 1, that is, bysubdividing all pixels into pixels having an image value above or belowa HU value of −400. The HU value of −400 is a suitable limit value formaking a decision between tissue structures on the one hand and ambientor trapped air and liquid on the other hand. Thus, in FIG. 2 the airtrapped in the lung, the air surrounding the body and the blood invessels appear in black, whereas the body surrounding the lung(essentially muscle tissue and bone tissue) and the heart in the lungsappear in white.

During a further filtering step a region growth is started in the spacesurrounding the body, so that the space surrounding the body is filledwith the same image value as the tissue structure. Consequently, thisspace, that is the ambient air as well as the patient table LU, appearsin white in FIG. 3.

Subsequently, as a first step of the first filtering operation inconformity with claim 1 the distance value of all previouslyfiltered-out white elements of FIG. 3 is determined and a correspondingdistance value is assigned to each pixel. The shortest distance betweenthe pixel and the edge of its image structure, that is, the shortestdistance between the pixel and the nearest black/white boundary in FIG.3, is then determined. Pixels at a very short distance from such aboundary are given a small distance value while pixels at a largedistance are assigned a large distance value. The distance values of thepixels are shown in FIG. 4. In FIG. 4 small distance values arerepresented by large gray scale values and large distance values arerepresented by small gray scale values; thus, pixels with a smalldistance value are dark and pixels with a large distance value arebright in FIG. 4.

According to a second step of the first filtering operation all pixelsare selected whose distance value is below a predetermined distancelimit value, for example, 30 mm. Thus, the further filtering steps takeinto account only pixels which belong to white image structures in FIG.3 whose dimensions are less than 60 mm or are situated at a distance ofless than 30 mm from black pixels.

During a second filtering operation, these pixels are tested as towhether they are enclosed, in at least two 180° mutually offsetdirections, exclusively by pixels whose distance value is smaller thanits own distance value. This is shown by way of example for two pixelsA, B in FIG. 5. Pixel A is situated at the center of a nodule which isfused with a wall. By way of example, four axes A1 to A4 are plotted soas to extend through the pixel A. As can be readily seen in FIG. 5, forexample, on the axis A4 in the direct vicinity of the pixel A there aresituated pixels whose distance value is smaller than the distance valueof the pixel itself. Therefore, the pixel A is selected during thesecond filtering operation.

Similarly, by way of example four axes with each time 180° mutuallyoffset directions are plotted so as to extend through the pixel B. Italso appears that on the axis B1 in the direct vicinity of the pixel Bthere are situated exclusively pixels whose distance value is smallerthan that of the pixel B. Therefore, the filter element B is alsoselected during the second filtering operation.

Referring to FIG. 5 again, in a third filtering operation first acircular image element boundary AG, BG is drawn around the pixels A, B,said boundary extending at a distance around the pixels which is inconformity with the distance value of the pixels A, B.

The averaged distance values of the extreme points of the axis of allaxes which are situated on the pixel boundaries AG and BG are comparedwith the distance values of the pixels A and B, respectively. As soon asduring this third filtering operation an axis is found on the pixelboundaries whose mean distance value is larger than the distance valueof the pixel A or B, the third filtering operation is interrupted andthe pixel A or B is removed from the previously made selection.

All axes surrounding the pixel element A on the pixel boundary AG have ameans distance value of the axis end points which is smaller than thedistance value of the pixel A. Therefore, the pixel A remains selectedduring the third filtering operation.

On the boundary BG surrounding pixel B there is situated at least oneaxis BE 1→BE2 whose mean distance value of the axis end points is notsmaller than the distance value of the pixel B. The distance values ofthe pixels BE1 and BE2 are equal to the distance value of the pixel B.Therefore, pixel B is not selected but rejected during the thirdfiltering operation.

The pixels that remain selected after the third filtering operation aresubjected to a fourth filtering operation. During this fourth filteringoperation a point-symmetrical region growth is started as from thesepixels A; to this end, reference is made to FIG. 6. FIG. 6 shows threegrowth phases of this point-symmetrical region growth. After the pixelsdirectly adjoining the pixel A have been included by the region growthin a growth phase (not shown), in a subsequent second growth phase theelements which directly adjoin the pixels directly adjoining the pixel Aare included by the region growth so that they form the growth envelopeWA1 in this second growth phase. The growth envelope WA1 contains twelvepixels.

During a third growth phase of the point-symmetrical region growth thepixels which externally adjoin the growth envelope WA1 are included bythe region growth and hence form the growth envelope WA2 in this thirdgrowth phase. The growth envelope WA2 comprises 16 pixels.

Subsequently, in a fourth growth phase the region growth includes 20pixels which form the growth envelope WA3 which adjoins the pixels ofthe growth envelope WA2.

The start element A is rejected during the fourth filtering operationand removed from the pixels selected after the third filtering operationif during the region growth a pixel is reached whose distance value islarger than the distance value of the start element A. Moreover, thepixel A is removed from the selection and rejected as soon as thesurface area included by the region growth, that is, the surface arelying within the growth envelope or the volume present therein, extendsin an arbitrary direction by more than five times the distance value ofthe start element A.

This second criterion leads to the interruption of the filtering inrespect of the start element A as soon as the growth region includes apixel which is situated more than five times the distance value from thestart element A. Furthermore, as a result of this criterion the startelement A is rejected and the fourth filtering in respect of the startelement A is interrupted as soon as two pixels are found which aresituated so as to be mutually offset 180° relative to the start elementA while the sum of the distances of these two pixels from the startelement A is larger than five times the distance value of the startelement A.

The start element A would be rejected on the basis of a fourth filteringas described thus far because, starting from the start element A andproceeding in a direction towards the wall structure, a pixel would befound which has a larger distance value than the start element A.However, this is undesirable, because the pixel A concerns a pixel of apotential nodular structure.

As a first step of a further filtering operation which is based on thefourth filtering operation the mean value of all pixels in the growthenvelope over the individual growth phases is plotted in conformity withthe FIGS. 8 and 14.

FIG. 8 shows the variation of the distance mean value for a startelement C, shown in FIG. 7, in an isolated nodule KI. In the diagram ofFIG. 8 it can be seen that, starting from the distance value 95 of thestart element C, the distance mean value first decreases during thesubsequent growth phases and then assumes, after a small increase, adistance mean value of approximately 30.

Referring to FIG. 14, the distance mean value of a start element D asshown in FIG. 13, being situated at the center of a nodule KA fused tothe lung wall, also decreases as before, that is, as from the distancevalue 37 of the start element D, passes through a minimum andsubsequently increases again. During this increase the distance meanvalue exceeds, approximately in the tenth growth phase, the distancevalue of the start element D. This is due to the fact that in the caseof the start element D the region growth in this growth phase alsoincludes pixels of the lung wall which have correspondingly highdistance values and hence increase the distance mean value beyond thedistance value of the start element.

The region growth is stopped as soon as the distance mean value of allpixels present within the growth envelope is larger than the distancevalue of the start element itself. Consequently, in the fifth filteringoperation the pixel D in the nodule KA fused to the lung wall would berejected, whereas the pixel C in the isolated nodule KI remainsselected. Furthermore, the fifth filtering operation rejects all pixelsbelonging to image structures which no longer belong to the core of afeasible lung tumor, but are only connected thereto.

In a further, sixth filtering operation, which is also based on thefourth filtering operation, the sum of all distance values of the pixelspresent in the growth envelope is plotted over the individual growthphases, so that variations in conformity with the FIGS. 9 and 15 areobtained for the start elements C and D.

For a start element C in an isolated nodule KI the curve thus plottedtypically extends in the manner shown in FIG. 9. After an initial rise,due to the increasing number of pixels present within the growthenvelope, a maximum Cmax is passed. After this maximum, the distancevalue sum decreases, due to the pixels with a small distance value whichare then included in the growth envelope, and reaches a local minimumCmin. For the rare case where the start element C is situated in anodule which is completely isolated in the image, the local minimum Cminat the same time constitutes the end point of the region growth, becausethe region growth meets the boundaries of the nodule at that instant sothat it ends. Regularly, however, the isolated nodules, for example ascan be seen in FIG. 7, are also traversed by vessels and hence connectedto other image structures. These image structures are also included bythe region growth and hence regularly lead to an increase of thedistance value sum, as shown in FIG. 9, after the passage of the minimumCmin. It has been found that the ratio of the distance value sum in thelocal minimum Cmin to the distance value sum in the previously occurringlocal maximum Cmax of the curve can be used as a criterion in decidingwhether the start element is situated in a potential nodular structure.It is particularly unlikely that the start element belongs to apotential nodular structure when the ratio of Cmin to Cmax is largerthan 0.35. In this case the start element is rejected and removed fromthe selection of the pixels remaining after the fifth filteringoperation.

It has also been found that the ratio of the distance mean values to thedistance value of the start element during the passage of Cmin can beused as a criterion in deciding whether the start element is situated ina potential nodular structure. This ratio of the distance mean valuescan be formed by transferring from FIG. 9 the growth phases of the localminimum Cmin to FIG. 8 and by reading on the y axis the distance meanvalue Cminmittel for these growth phases. When the ratio of the distancemean value in the local minimum, that is, Cminmittel, to the distancevalue of the start element is higher than 0.5, the start element is notsituated within a potential nodular structure so that it can be rejectedand removed from the selection of pixels remaining after the fifthfiltering operation.

The two previously mentioned criteria of the sixth filtering operationremove further pixels which belong to image structures having a geometrywhich is not typical of nodular structures from the selection of pixelsremaining after the fifth filtering operation.

It is a drawback of the fifth and the sixth filtering operation, asdescribed before, that the filter criteria also reject pixels whichbelong to nodular structures fused to larger image structures. FIG. 13shows such a nodular structure. The variation of the distance mean valueand of the distance value sum associated with this nodular structure KAare shown in the FIGS. 14 and 15, respectively. It can be seen that thestart element D in the nodular structure KA was rejected on the basis ofthe filter criteria of the fifth and the sixth filtering operation. Apotential nodular structure would thus be removed from the selection.

In order to avoid the foregoing, a boundary is drawn between the nodularstructure and the wall with which said nodular structure is fused, afterwhich the fifth and the sixth filtering operation are carried out again,pixels situated beyond the boundary then being excluded from the regiongrowth.

The drawing of the boundary between the nodule and the wall will bedescribed with reference to the FIGS. 10 to 12. First all pixels areconsidered which are situated on the growth envelope WH in that growthphase in which one of said criteria of the fifth or the sixth filteringoperation led to the interruption of the region growth and the rejectionof the start element D. These pixels belong either to the lung wall LWor to vessels G1, G2 extending through the nodule KA. Using designationof the connected components (connected component labeling), from thesepixels there are determined those pixels which belong to the largestcoherent growth envelope structure. From the pixels of the largestgrowth envelope segment WHG thus determined, there are determined thosepixels which are situated at the boundary of the lung wall structure,that is, WHG1 and WHG2. The pixels WHG1 and WHG2 are determined from thenumber of pixels which are situated within the envelope segment WHG,that is, by searching those pixels which directly adjoin pixels having adistance value equal to 0. A spatial co-variant matrix is calculatedfrom the image structure edge elements WHG1 and WHG2 thus determined,and a main component analysis is carried out, yielding three eigenvaluesand one center M, M being situated at the origin of the vector Ev on theplane LWG.

The eigenvector Ev corresponding to the smallest eigenvalue is taken asthe normal to the surface of the lung wall LW. The direction of thislung wall normal is set to be such that it points inwards in thedirection of the lung, that is, from the lung wall boundary LWG,determined by the eigenvector, in the direction of the node KA. Thisorientation can be tested by summing the projections of the pixels inthe growth envelope WH on the lung wall normal. This sum must benegative when the lung wall normal vector points into the lung. In theother case the normal vector must be reversed by multiplication by −1.

After the lung wall boundary LWG has been determined in the describedmanner, the point-symmetrical region growth around the start element D(as described before) is repeated, subject to the additional conditionthat the region growth should not include any pixels which, viewed fromthe start element D, are situated to the other side of the lung wallboundary LWG.

When the region growth is repeated with this additional condition asregards the start element D shown in FIG. 13, the variations of thedistance mean value and the distance value sum will be as shown in FIG.16 and FIG. 17. A similar result would also be obtained if the regiongrowth were repeated, after the corresponding drawing of the boundary,for the start element A shown in FIG. 5. The variations in conformitywith FIG. 16 and FIG. 17 reveal that in this case none of the criteriaof the fifth and the sixth filtering operation is satisfied, so that inconformity with the filtering operation thus modified the removal andrejection of the relevant start element from the selection of pixels ofpotential nodular structures is avoided.

After completion of all filtering steps, pixels which represent thecenter of potential nodular structures remain in the selection. In theimage in conformity with FIG. 1 or FIG. 2 these pixels can be marked inwhite, so that subsequently an image is obtained in conformity with theFIGS. 18 and 19 in which these pixels are highlighted so that they canbe readily discovered by a viewer.

1. A method of forming an image of body structures from an image dataset, notably for highlighting potential nodular structures in a lung,which method includes the steps of a) forming a binary data set in whichthe pixels present in the image data set are subdivided into pixelswhich are to be marked and those which are not to be marked, in whichstep a1. a first filtering operation is performed in which a distancevalue is determined for each pixel, which distance value corresponds tothe shortest distance between the pixel and the edge of the imagestructure in which the pixel is situated, and in which those pixels areselected from the binary data set whose distance value is below apredetermined distance limit value, a2. a second filtering operation isperformed in which those previously selected pixels remain selectedwhich are directly neighbored in both directions of at least onestraight line, extending through the pixel, by two pixels having asmaller distance value, a3. a third filtering operation is performed inwhich those previously selected pixels remain selected for which thesurrounding pixels, situated at a distance corresponding exactly to thedistance value of the pixel, have a distance value which is apredetermined distance difference value smaller than the distance valueof the pixel to be tested itself, b) forming a marked image data setfrom the image data set by marking the pixels having been selected afterthe last filtering operation in the binary data set, and c) forming theimage of the body structure from the marked image data set.
 2. A methodas claimed in claim 1, characterized in that filtering by means of limitvalues of the image values is performed prior to the first filteringoperation.
 3. A method as claimed in claim 1, characterized in thatduring the third filtering operation the pixel to be tested is rejectedas not to be marked as soon as a surrounding pixel is found whosedistance value is the predetermined distance difference value smallerthan the distance value of the pixel to be tested itself.
 4. A method asclaimed in claim 1 or 3, characterized in that the predetermineddistance difference value is zero.
 5. A method as claimed in claim 1,characterized in that it comprises a fourth filtering operation in whichregion growth is started as from the previously selected pixels whichact as a start element, and that the pixel to be tested is rejected asnot to be marked: as soon as the region growth reaches a pixel whosedistance value is larger than the distance value of the start elementand/or when the image volume included by the region growth extendsfurther in one direction than a predetermined volume dimension valuewhich is preferably the distance value of the start element.
 6. A methodas claimed in claim 5, characterized in that the predetermined volumedimension value corresponds to five times the distance value of thestart element.
 7. A method as claimed in claim 5, characterized in thatthe region growth takes place point-symmetrically relative to the startelement and that the start element is rejected as not to be marked assoon as the mean value of the distance values of the pixels included islarger than a predetermined distance mean value which preferablycorresponds to the distance value of the start element.
 8. A method asclaimed in claim 5, characterized in that the region growth takes placepoint-symmetrically relative to the start element and that the startelement is selected so as to be marked or not to be marked on the basisof an evaluation of the variation of the distance value sum,corresponding to the sum of the distance values of the pixels includedby the region growth, during the progression of the region growth.
 9. Amethod as claimed in claim 8, characterized in that the variation isplotted as a curve representing the variation of the distance value sumduring the progression of the region growth and that the start elementis rejected if the extreme value quotient of a local minimum of thecurve and a previously occurring local maximum of the curve is largerthan a predetermined extreme value quotient, preferably being one,and/or if the mean value quotient of the mean value of the distancevalues of the pixels included by the region growth until the localminimum is reached and the distance value of the pixel to be tested islarger than a predetermined mean value quotient, preferably being 0.5.10. A method as claimed in claim 9, characterized in that thepredetermined extreme value quotient amounts to 0.35.
 11. A method asclaimed in one of the claims 5 to 9, characterized in that it comprisesa fifth filtering operation in which the pixels which have not beenselected during the fourth filtering operation are selected if theybelong to a first image structure which is fused with a second imagestructure, in that the boundary between the first and the second imagestructure is approached, the region growth in conformity with the claims5 to 9 is repeated, those pixels which are situated, viewed from thestart element, beyond the boundary then remaining excluded from theregion growth, and the start element is selected if none of theconditions in conformity with the claims 4 to 8 is satisfied.
 12. Amethod as claimed in claim 11, characterized in that the boundary isdetermined in that a spatial co-variant matrix is calculated from allpixels which are situated at the edge of the region growth in the growthphase in which in the fourth filtering operation one of the conditionsin conformity with the claims 5 to 9 was satisfied, and whose distancevalue is below a given value or which are situated in the edge zone ofan image structure, a main component analysis is calculated from theco-variant matrix and the eigenvector of the smallest eigenvalueresulting from the main component analysis is used as the surface normalto the boundary.
 13. A device for forming an image of body structuresfrom an image data set, notably for highlighting potential nodularstructures in a lung, which device comprises a) means for forming abinary data set by subdividing the pixels present in the image data setinto pixels which are to be marked and those which are not to be marked,including a1. means for performing a first filtering operation in whicha distance value of the previously filtered pixels is determined, whichdistance value corresponds to the shortest distance between the pixel tobe tested and the edge of the image structure in which the pixel to betested is situated, those previously filtered pixels of the binary dataset being selected whose distance values are below a predetermineddistance limit value, a2. means for performing a second filteringoperation in which those previously filtered pixels are selected whichare surrounded only by pixels having a smaller distance value at adistance which corresponds to or is smaller than the distance value ofthe pixel at least along one straight line which extends through thepixel, a3. means for performing a third filtering operation during whichthose previously filtered pixels are selected for which the surroundingpixels, being situated at a distance corresponding to the distance valueof the pixel, have a distance value which is a predetermined distancedifference value smaller than the distance value of the pixel to betested itself, b) means for forming a marked image data set from theimage data set by marking the pixels having been selected after the lastfiltering operation in the binary data set, and c) means for forming theimage of the body structure from the marked image data set.
 14. Acomputer program for forming an image of body structures from an imagedata set, including programming means which carry out the steps of themethod disclosed in claim 1 when run on a computer.